Consumer behaviors at lender level

ABSTRACT

The present disclosure generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present disclosure relates to rating consumer lenders based on the predicted spend capacity of their consumers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 12/976,682, filed Dec. 22, 2010, and entitled,“Consumer Behaviors at the Lender Level.” The '682 Application is acontinuation of and claims priority to U.S. patent application Ser. No.12/058,378, filed Mar. 28, 2008, and entitled, “Consumer Behaviors atthe Lender Level.” The contents of the '682 Application and the '378Application are incorporated by reference herein in their entirety.

FIELD

The disclosure generally relates to financial data processing, and moreparticularly, to lender credit scoring, lender profiling, lenderbehavior analysis and modeling.

BACKGROUND

An ability to assess the risk levels associated with various lenders,and consumers who deal with those lenders, could allow other entities tobetter manage their risk. In addition, risk level data could allow afinancial institution (such as a credit company, lender or any consumerservices companies) to better target potential prospects and identifyany opportunities to increase consumer transaction volumes, without anundue increase in the risk of defaults. Better assessing risk, in turn,may increase a financial institution's revenues, primarily in the formof an increase in transaction fees and interest payments received.Consequently, a consumer model that can accurately estimate risk ofdefault by lender is often of paramount interest to many financialinstitutions and other consumer services companies. To serve thesepurposes, a consumer model that can accurately estimate consumerspending capacity for consumers associated with a particular lender isof typically of paramount interest to many financial institutions andother consumer services companies.

Accordingly, there is a need for a method and a system for modeling arisk level associated with a particular lender that addresses certainproblems of existing technologies. There is also a need for a method andsystem for predicting consumer spend associated with a particular lenderthat addresses certain problems of existing technologies.

SUMMARY

The present disclosure includes an account default prediction method.The method comprises, in one embodiment, obtaining a first consumerdefault risk factor associated with a first consumer, acquiring firstloan data associated with a first consumer, wherein the first loan datais associated with a first lender, obtaining a second consumer defaultrisk factor associated with a second consumer, acquiring second loandata associated with a second consumer, wherein the second loan data isassociated with a first lender, calculating a first lender default riskfactor based upon the first consumer default risk factor and the secondconsumer default risk factor, obtaining a third consumer default riskfactor associated with a third consumer, acquiring third loan dataassociated with a third consumer, wherein the third loan data isassociated with a second lender, obtaining a fourth consumer defaultrisk factor associated with a fourth consumer, acquiring fourth loandata associated with a fourth consumer, wherein the fourth loan data isassociated with the second lender, calculating a second lender defaultrisk factor based upon the third consumer default risk factor and thefourth consumer default risk factor and ranking the first lender and thesecond lender based on the first lender default risk factor and thesecond lender default risk factor to create a default risk factorranking. In such embodiments, the present disclosure may additionallyinclude receiving a request from a fifth consumer for an account,determining when the fifth consumer is associated with the first lenderand/or the second lender, and determining the account default predictionbased upon the association of the fifth consumer with the first lenderand/or the second lender. The method may further include wherein theobtaining a first consumer default risk factor further comprisescalculating a comprehensive consumer default risk value. Calculating acomprehensive consumer default risk value may further comprise obtainingconsumer credit data relating to the first consumer, modeling consumerspending patterns of the first consumer using the consumer credit datato obtain an estimated spend capacity of the first consumer andcalculating a comprehensive consumer default risk value for the firstconsumer based upon the consumer credit data and the estimated spendcapacity. Calculating a comprehensive consumer default risk value mayfurther comprise obtaining internal data relating to the first consumer;and further calculating the comprehensive consumer default risk valuefor the first consumer based upon the consumer credit data, the internaldata and the estimated spend capacity.

The present disclosure also provides a method of consumer spendprediction. The method comprises, in one embodiment, obtaining a firstconsumer spending pattern associated with a first consumer, acquiringfirst loan data associated with a first consumer, wherein the first loandata is associated with a first lender, obtaining a second consumerspending pattern associated with a second consumer, acquiring secondloan data associated with a second consumer, wherein the second loandata is associated with a first lender, calculating a first lenderspending pattern based upon the first consumer spending pattern and thesecond consumer spending pattern, obtaining a third consumer spendingpattern associated with a third consumer, acquiring third loan dataassociated with a third consumer, wherein said the loan data isassociated with a second lender, obtaining a fourth consumer spendingpattern associated with a fourth consumer, acquiring fourth loan dataassociated with a fourth consumer, wherein the fourth loan data isassociated with a second lender, calculating a second lender spendingpattern based upon the third consumer spending pattern and said fourthconsumer spending pattern and ranking the first lender and the secondlender based on the first lender spending pattern and the second lenderspending pattern to create a lender spending pattern ranking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of exemplary categories of consumers, in accordancewith one embodiment;

FIG. 2 is a diagram of exemplary subcategories of consumers, inaccordance with one embodiment;

FIG. 3 is a diagram of exemplary financial data used for modelgeneration and validation, in accordance with one embodiment;

FIG. 4 is a flowchart of an exemplary process for estimating the spendability of a consumer, in accordance with one embodiment;

FIG. 5 is a flow diagram of an exemplary method for consumer defaultprediction, in accordance with one embodiment;

FIG. 6 is a flow diagram of an exemplary method for consumer spendprediction, in accordance with one embodiment;

FIG. 7 is a flowchart of an exemplary process for modeling consumerdefault risk;

FIG. 8 is a flowchart of an exemplary process for calculating acomprehensive consumer default risk value.

DETAILED DESCRIPTION

The detailed description of exemplary embodiments herein makes referenceto the accompanying drawings and pictures, which show the exemplaryembodiment by way of illustration and its best mode. While theseexemplary embodiments are described in sufficient detail to enable thoseskilled in the art to practice the invention, it should be understoodthat other embodiments may be realized and that logical and mechanicalchanges may be made without departing from the spirit and scope of theinvention. Thus, the detailed description herein is presented forpurposes of illustration only and not of limitation. For example, thesteps recited in any of the method or process descriptions may beexecuted in any order and are not limited to the order presented.Moreover, any of the functions or steps may be outsourced to orperformed by one or more third parties. Furthermore, any reference tosingular includes plural embodiments, and any reference to more than onecomponent may include a singular embodiment.

The present disclosure comprises a financial data processing system andmethod. In one embodiment, the system and method includes lender creditscoring, lender profiling, lender behavior analysis and/or modeling. Thesystem and method also includes rating consumer lenders based on dataderived from their respective consumers, predicting account defaultand/or predicting consumer spend. Any of the methods may use external orinternal data.

Internal data includes any data a credit issuer possesses or acquirespertaining to a particular consumer. Internal data may be gatheredbefore, during, or after a relationship between the credit issuer andthe consumer. Such data may include consumer demographic data. Consumerdemographic data includes any data pertaining to a consumer. Consumerdemographic data may include consumer name, address, telephone number,email address, employer and social security number. Consumertransactional data is any data pertaining to the particular transactionsin which a consumer engages during any given time period. Consumertransactional data may include transaction amount, transaction time,transaction vendor/merchant, and transaction vendor/merchant location.Transaction vendor/merchant location may contain a high degree ofspecificity to a vendor/merchant. For example, transactionvendor/merchant location may include a particular gasoline filingstation in a particular postal code located at a particular crosssection or address. Also for example, transaction vendor/merchantlocation may include a particular web address, such as a UniformResource Locator (“URL”), an email address and/or an Internet Protocol(“IP”) address for a vendor/merchant. Transaction vendor/merchantlocation may also include information gathered from a WHOIS databasepertaining to the registration of a particular web or IP address. WHOISdatabases include databases that contain data pertaining to Internet IPaddress registrations. Transaction vendor/merchant, and transactionvendor/merchant location may be associated with a particular consumerand further associated with sets of consumers. Consumer payment dataincludes any data pertaining to a consumer's history of paying debtobligations. Consumer payment data may include consumer payment dates,payment amounts, balance amount, and credit limit. Internal data mayfurther comprise records of consumer service calls, complaints, requestsfor credit line increases, questions, and comments. A record of aconsumer service call includes, for example, date of call, reason forcall, and any transcript or summary of the actual call.

Internal data may further comprise closed-loop data and open-loop data.Closed-loop data includes data obtained from a credit issuer'sclosed-loop transaction system. A closed-loop transaction systemincludes transaction systems under the control of one party. Closed-looptransaction systems may be used to obtain consumer transactional data.Open-loop data includes data obtained from a credit issuer's open-looptransaction system. An open-loop transaction system includes transactionsystems under the control of multiple parties.

Credit bureau data includes any data retained by a credit bureaupertaining to a particular consumer. A credit bureau includes anyorganization that collects and/or distributes consumer data. A creditbureau may be a consumer reporting agency. Credit bureaus generallycollect financial information pertaining to consumers. Credit bureaudata may include, for example, consumer account data, credit limits,balances, and payment history. Credit bureau data may include creditbureau scores that reflect a consumer's creditworthiness. Credit bureauscores are developed from data available in a consumer's file such as,for example, the amount of lines of credit, payment performance,balance, and number of tradelines. Consumer data is used to model therisk of a consumer over a period of time using statistical regressionanalysis. In one embodiment, those data elements that are found to beindicative of risk are weighted and combined to determine the creditscore. For example, each data element may be given a score, with thefinal credit score being the sum of the data element scores.

A debt obligation includes any obligation a consumer has to pay alender. Any extension of credit from a lender to a consumer is alsoconsidered a debt obligation. A debt obligation may be secured orunsecured. Secured obligations may be secured with either real orpersonal property. A loan or a credit account are types of debtobligations, and a security backed by debt obligations is considered adebt obligation itself. A mortgage includes a loan, typically in theform of a promissory note, secured by real property. The real propertymay be secured by any legal means, such as, for example, via a mortgageor deed of trust. For convenience, a mortgage is used herein to refer toa loan secured by real property. An automobile loan includes a loan,typically in the form of a promissory note, which is secured by anautomobile. For convenience, an automobile loan is used herein to referto a loan secured by an automobile.

A trade or tradeline includes a credit or charge vehicle typicallyissued to an individual consumer by a credit grantor. Types oftradelines include, for example, bank loans, credit card accounts,retail cards, personal lines of credit and car loans/leases.

Tradeline data includes the consumer's account status and activity suchas, for example, names of companies where the consumer has accounts,dates such accounts were opened, credit limits, types of accounts,balances over a period of time and summary payment histories. Tradelinedata is generally available for the vast majority of actual consumers.Tradeline data, however, typically does not include individualtransaction data, which is largely unavailable because of consumerprivacy protections. Tradeline data may be used to determine bothindividual and aggregated consumer spending patterns, as describedherein.

A trade or tradeline includes a credit or charge vehicle issued to anindividual consumer by a credit grantor. Types of tradelines include,for example, bank loans, credit card accounts, retail cards, personallines of credit and car loans/leases. The term credit card shall beconstrued to include charge cards except as specifically noted.Tradeline data describes the consumer's account status and activity,including, for example, names of companies where the consumer hasaccounts, dates such accounts were opened, credit limits, types ofaccounts, balances over a period of time and summary payment histories.Tradeline data is generally available for the vast majority of actualconsumers. Tradeline data, however, may not include individualtransaction data, which is largely unavailable because of consumerprivacy protections. Tradeline data may be used to determine bothindividual and aggregated consumer spending patterns, as describedherein.

Any transaction account or credit account discussed herein may includean account or an account number. An “account” or “account number”, asused herein, may include any device, code, number, letter, symbol,digital certificate, smart chip, digital signal, analog signal,biometric or other identifier/indicia suitably configured to allow theconsumer to access, interact with or communicate with the system (e.g.,one or more of an authorization/access code, personal identificationnumber (PIN), Internet code, other identification code, and/or thelike). The account number may optionally be located on or associatedwith a rewards card, charge card, credit card, debit card, prepaid card,telephone card, embossed card, smart card, magnetic stripe card, barcode card, transponder, radio frequency card or an associated account.The system may include or interface with any of the foregoing cards ordevices, or a fob having a transponder and RFID reader in RFcommunication with the fob. Although the system may include a fobembodiment, the disclosure is not to be so limited. Indeed, system mayinclude any device having a transponder which is configured tocommunicate with RFID reader via RF communication. Typical devices mayinclude, for example, a key ring, tag, card, cell phone, wristwatch orany such form capable of being presented for interrogation. Moreover,the system, computing unit or device discussed herein may include a“pervasive computing device,” which may include a traditionallynon-computerized device that is embedded with a computing unit. Examplescan include watches, Internet enabled kitchen appliances, restauranttables embedded with RF readers, wallets or purses with imbeddedtransponders, etc.

A lender includes any person, entity, software and/or hardware thatprovides lending services. A lender may deal in secured or unsecureddebt obligations. A lender may engage in secured debt obligations whereeither real or personal property acts as collateral. A lender need notoriginate loans but may hold securities backed by debt obligations. Alender may be only a subunit or subdivision of a larger organization. Amortgage holder includes any person or entity that is entitled torepayment of a mortgage. An automobile loan holder is any person orentity that is entitled to repayment of an automobile loan. As usedherein, the terms lender and credit issuer may be used interchangeably.Credit issuers may include financial services companies that issuecredit to consumers.

As used herein, an account default prediction method includes a methodof determining the risk of default to a credit issuer for a givenconsumer. Risk of default is the likelihood a given consumer will failto repay a given debt obligation. An account default prediction methodgenerally quantifies risk based on a variety of factors. An accountdefault prediction method may quantify default risk based on a consumerassociation with a given lender and/or may comprise ranking a given setof lenders based on consumer data. An account default prediction methodmay also comprise determining an account default prediction based uponan association of a consumer with one or a set of given lenders.

Referring to FIG. 5, a consumer default risk score 801, 802, 806, 807includes a value that describes the risk that a consumer may default ona given loan or other debt obligation. A consumer default risk score801, 802, 806, 807 may be derived from any data pertaining to aconsumer. These data include, for example, consumer demographic data,debt obligation history, debt obligation payment history, debtobligation insufficiency data, history of bankruptcy, income data, andany other data pertaining to the financial health of a consumer. Aconsumer default risk score 801, 802, 806, 807 may be calculated in anymanner.

One method of determining a consumer default risk score is to request aconsumer default risk score from a provider such as the Fair IsaacCorporation of Minneapolis, Minn.

One method of determining a consumer default risk score is to determinea comprehensive consumer default risk value. Methods and systems fordetermining a comprehensive consumer default risk value have beendisclosed in U.S. patent application Ser. No. 12/040,742, the disclosureof which is hereby incorporated by reference in its entirety. Exemplarymethods of calculating a comprehensive consumer default risk value willnow be discussed in detail. A comprehensive consumer default risk valueis a value that describes the risk that a consumer will default on anydebt obligation. The debt obligation may be held by any lender or creditissuer. Calculating the comprehensive consumer default risk value can bedone by any suitable means.

In various embodiments, the comprehensive consumer default risk value iscalculated using a SoW output, as described herein below, combined withcredit bureau data. In various embodiments, internal data may be used inaddition to a SoW output and credit bureau data.

In various embodiments, calculating the comprehensive consumer defaultrisk value may involve, as depicted in FIG. 7, obtaining consumer creditdata 701, modeling consumer spending patterns 702, and calculating acomprehensive consumer default risk value 703. Calculating thecomprehensive consumer default risk value may also involve obtaininginternal data for a given consumer 704.

Consumer credit data 701 may be obtained from any source such as, forexample, a credit bureau. Modeling consumer spending patterns mayinclude any process or method designed to assess the spending pattern ofa consumer such as, for example, the SoW model.

Calculating the comprehensive consumer default risk value 703 mayinvolve the process depicted in FIG. 8, namely, assigning a consumerpopulation segment 750, selecting an appropriate risk factorrelationship 760 and deriving a default probability 770 based upon saidrisk factor relationship. Assigning a consumer population segment 750includes any method for assigning consumers into population segments. Aconsumer population segment 750 may be based upon, for example, highrisk consumers and low risk consumers categories. A consumer populationsegment 750 may be based upon primary residence value. Selecting anappropriate risk factor relationship 760 may include any method ofcreating a relationship between risk factors. Selecting an appropriaterisk factor relationship 760 may be dependent upon the assigned consumerpopulation segment. Risk factors include any method of assessing risk.Risk factors may include risk factors derived from credit bureau data,internal data, merchant data, or any other factor that may be predictiveof risk. A risk factor relationship 760 may take the form of, forexample, an equation. An equation includes linear, exponential, andlogarithmic equations. An equation may assign fixed coefficientsassociated with a particular risk factor. A coefficient in an equationmay vary depending upon the particular consumer population segmentassigned. Deriving a default probability 770 based upon said risk factorrelationship 760 may take the form of, for example, an equation. Anequation includes linear, exponential, and logarithmic equations. Forexample, a logarithmic equation may transform a risk relationship into adefault probability 770. A default probability 770 may take the form ofa probability value between 0 and 1.

A lender default risk score 805, 810 includes a composite value ofconsumer default risk scores of consumers who have debt obligations witha given lender. A lender default risk score 805, 810 can be calculatedin any suitable method. Suitable methods include calculating the mean,median, and/or mode of a set of consumer default risk scores 801, 802,806, 807. Suitable methods also include other calculations such ascalculating a root-mean-square (quadratic mean) or a sum of a set ofconsumer default risk scores 801, 802, 806, 807.

A ranking of lenders 811 includes a ranking of lenders based on theirrespective lender default risk scores. A ranking of lenders may beordinal in character, with lenders ranked from most risk to least riskor from least risk to most risk.

Loan data 803, 804, 808, 809 includes any data pertaining to a loan fora given consumer. Loans may be of any type, for example, a mortgage, astudent loan, and automobile loan. Loan data 803, 804, 808, 809 mayinclude, among other things, loan balance, loan payment history, loandelinquencies and loan origination date. Loan data 803, 804, 808, 809may be obtained through any legal means. Loan data 803, 804, 808, 809may be obtained through one or more credit bureaus. Loan data 803, 804,808, 809 may be obtained from a lender.

Receiving a request from a consumer 812 includes any receipt of aconsumer request for a debt obligation. The request may be, for example,for a mortgage, a student loan, a credit card, a charge card, or anautomobile loan. Determining the consumer lender association 815involves determining if a consumer has an association with a knownlender and then determining the lender default risk ranking of thelender. The association could be an existing, present, or projectedfuture relationship between the consumer and the lender. Predictingdefault risk based upon the relationship of the consumer and the lender816 involves using the lender ranking to predict the likelihood aconsumer will default on the debt obligation he requested.

Loan insufficiencies include any negative credit events during thecourse of repayment in a debt obligation life cycle. Loaninsufficiencies include, for example, payment delinquency data,foreclosures, bankruptcy history, and any other data regarding debtrepayment that reflects negatively on the debtor's ability to repay. Forexample, an insufficiency may be a value derived from the most recentninety days of payment history on a debt obligation. Further forexample, the consumers with loan insufficiencies in the last ninety daysmay be identified. The number of consumers with loan insufficiencies inthe last ninety days may be aggregated and a percentage of theseconsumers with respect to all a lender's consumers may be obtained. Invarious embodiments, the payment history of a debt obligation is thepayment history of a mortgage.

Loan insufficiencies may be used in conjunction with loan data 803, 804,808, 809 and consumer default risk score 801, 802, 806, 807 to calculatelender default risk score 805, 810. In various embodiments, loaninsufficiency data may weigh more heavily than the consumer default riskscore in the lender default risk score 805, 810 calculation. Forexample, if the average consumer default risk score for a given lenderis low but the percentage of loan insufficiencies is high, the averageconsumer default risk score may be disregarded. Also for example, if theaverage consumer default risk score for a given lender is low but thepercentage of loan insufficiencies is high, the average consumer defaultrisk score may be discounted.

The present disclosure may also provide methods of scoring and/orranking lenders in a manner that can predict the spending patternsassociated with their consumers.

Consumer spending patterns are modeled in any suitable manner. Modelingmay include determining consumer Size of Wallet (“SoW”), as describedherein below. Consumer SoW may be modeled using consumer associationswith one or a set of lenders. In various embodiments, the presentdisclosure provides a consumer SoW score 901, 902, 903, 904, asdescribed below, which may be obtained for a given consumer.

A lender SoW score 905, 906 includes a composite value of consumer SoWoutput of consumers who have debt obligations with a given lender. Alender SoW score 905, 906 can be calculated in any suitable method.Suitable methods could be calculating the mean, median, or mode of a setof consumer SoW scores. Suitable methods could also include othercalculations such as calculating a root-mean-square (quadratic mean) ora sum of a set of consumer SoW scores.

A ranking of lenders 907 includes a ranking of lenders based on theirrespective lender SoW scores 905, 906. A ranking of lenders may beordinal in character, with lenders ranked from highest lender SoW scoreto lowest lender SoW score or lowest lender SoW score to highest lenderSoW score.

Receiving a request from a consumer 908 includes any receipt of aconsumer request for a debt obligation. The request 908 could be, forexample, for a mortgage, a student loan, a credit card or bank card, acharge card, a retail card or an automobile loan. Determining theconsumer lender association 909 involves determining if a consumer hasan association with a known lender and then determining the ranking ofthe lender. The association could be an existing, present, or projectedfuture relationship between the consumer and the lender. Determiningconsumer spend prediction 910 based upon the relationship of theconsumer and the lender involves using the lender ranking to predictconsumer spend.

To model consumer spending power, consumer spend may be determined overprevious periods of time (sometimes referred to herein as the consumer'ssize of wallet) from tradeline data sources. The share of wallet bytradeline or account type may also be determined. The size of wallet(“SoW”) is represented by a consumer's or business' total aggregatespending and the share of wallet represents how the consumer usesdifferent payment instruments. Methods and apparatus for calculating thesize of wallet have been disclosed in U.S. patent application Ser. No.11/169,588 which was published with publication number 2006-0242046 A1,the disclosure of which is hereby incorporated by reference in itsentirety. Methods and apparatus for calculating the size of wallet havealso been disclosed in U.S. patent application Ser. No. 11/586,737 whichwas published with publication number US 2007-0226130 A1, the disclosureof which is hereby incorporated by reference in its entirety. Exemplarysize of wallet determinations will now be discussed in detail.

Consumer panel data measures consumer spending patterns from informationthat is provided by, typically, millions of participating consumerpanelists. Exemplary consumer panel data is available through variousconsumer research companies, such as comScore Networks, Inc. of Reston,Va. Consumer panel data may include individual consumer information suchas, for example, credit risk scores, credit card application data,credit card purchase transaction data, credit card statement views,tradeline types, balances, credit limits, purchases, balance transfers,cash advances, payments made, finance charges, annual percentage ratesand fees charged. Such individual information from consumer panel data,however, may be limited to those consumers who have participated in theconsumer panel, and so such detailed data may not be available for allconsumers. One skilled in the art will appreciate that the use of theterm “computer” or any similar term includes any type of hardware orsoftware in which a host is able to acquire information. Such computersmay include personal computers, personal digital assistants, biometricdevices, transaction account devices, loyalty accounts and/or the like.

As shown in FIG. 1, a population of consumers for which individualand/or aggregated data has been provided may be divided into two generalcategories for analysis, for example, those that are current on theircredit accounts (representing 1.72 million consumers in the exemplarydata sample size of 1.78 million consumers) and those that aredelinquent (representing 0.06 million of such consumers). In oneembodiment, delinquent consumers may be discarded from the populationsbeing modeled.

In further embodiments, the population of current consumers issubdivided into a plurality of further categories based on the amount ofbalance information available and the balance activity of such availabledata. In the example shown in FIG. 1, the amount of balance informationavailable is represented by a string of ‘+’ ‘0’ and ‘?’ characters. Eachcharacter represents one month of available data, with the rightmostcharacter representing the most current months and the leftmostcharacter representing the earliest month for which data is available.In the example provided in FIG. 1, a string of six characters isprovided, representing the six most recent months of data for eachcategory. The ‘+” character represents a month in which a credit accountbalance of the consumer has increased. The “0” character may representmonths where the account balance is zero. The “?” character representsmonths for which balance data is unavailable. Also provided in FIG. 1 isnumber of consumers that fall into each category and the percentage ofthe consumer population they represent in that sample.

In further embodiments, only certain categories of consumers may beselected for modeling behavior. The selection may be based on thosecategories that demonstrate increased spend on their credit balancesover time. However, it should be readily appreciated that othercategories can be used. FIG. 1 shows an example of two categories ofselected consumers for modeling (+++++, ???+++). These groups show theavailability of at least the three most recent months of balance dataand that the balances increased in each of those months.

Turning now to FIG. 2, which shows sub-categorization of the twocategories (+++++, ???+++) that are selected for modeling. In theembodiment shown, the sub-categories may include: consumers having amost recent credit balance less than $400; consumers having a mostrecent credit balance between $400 and $1600; consumers having a mostrecent credit balance between $1600 and $5000; consumers whose mostrecent credit balance is less than the balance of, for example, threemonths ago; consumers whose maximum credit balance increase over, forexample, the last twelve months divided by the second highest maximumbalance increase over the same period is less than 2; and consumerswhose maximum credit balance increase over the last twelve monthsdivided by the second highest maximum balance increase is greater than2. It should be readily appreciated that other subcategories can beused. Each of these subcategories is defined by their last month balancelevel. The number of consumers from the sample population (in millions)and the percentage of the population for each category are also shown inFIG. 2.

There may be a certain balance threshold established, wherein if aconsumer's account balance is too high, their behavior may not bemodeled, since such consumers are less likely to have sufficientspending ability. In another embodiment, consumers having balances abovesuch threshold may be sub-categorized yet again, rather than completelydiscarded from the sample. In the example shown in FIG. 2, the thresholdvalue may be $5000, and only those having particular historical balanceactivity may be selected, i.e. those consumers whose present balance isless than their balance three months earlier, or whose maximum balanceincrease in the examined period meets certain parameters. Otherthreshold values may also be used and may be dependent on the individualand aggregated consumer data provided.

The models generated may be derived, validated and refined usingtradeline and consumer panel data. An example of tradeline data 500 fromExperian and consumer panel data 502 from comScore is represented inFIG. 3. Each row of the data represents the record of one consumer andthousands of such records may be provided at a time. The statement showsthe point-in-time balance of consumers accounts for three successivemonths (Balance 1, Balance 2 and Balance 3). The data shows eachconsumer's purchase volume, last payment amount, previous balance amountand current balance. Such information may be obtained, for example, bypage scraping the data (in any of a variety of known manners usingappropriate application programming interfaces) from an Internet website or network address at which the data is displayed.

Furthermore, the data may be matched by consumer identity and combinedby one of the data providers or another third party independent of thefinancial institution. Validation of the models using the combined datamay then be performed, and such validation may be independent ofconsumer identity.

Turning now to FIG. 4, an exemplary process for estimating the size ofan individual consumer's spending wallet is shown. Upon completion ofthe modeling of the consumer categories above, the process commenceswith the selection of individual consumers or prospects to be examined(step 602). An appropriate model derived for each category will then beapplied to the presently available consumer trade line information inthe following manner to determine, based on the results of applicationof the derived models, an estimate of a consumer's size of wallet. Eachconsumer of interest may be selected based on their falling into one ofthe categories selected for modeling described above, or may be selectedusing any of a variety of criteria.

The process continues to step 604 where, for a selected consumer, apaydown percentage over a previous period of time is estimated for eachof the consumer's credit accounts. In one embodiment, the paydownpercentage is estimated over the previous three-month period of timebased on available tradeline data, and may be calculated according tothe following formula:Pay-down %=(The sum of the last three months payments from theaccount)/(The sum of three month balances for the account based ontradeline data).

The paydown percentage may be set to, for example, 2%, for any consumerexhibiting less than a 5% paydown percentage, and may be set to 100% ifgreater than 80%, as a simplified manner for estimating consumerspending behaviors on either end of the paydown percentage scale.

Consumers that exhibit less than a 50% paydown during a three monthperiod may be categorized as revolvers, while consumers that exhibit a50% paydown or greater may be categorized as transactors. Thesecategorizations may be used to initially determine what, if any,purchasing incentives may be available to the consumer, as describedlater below.

The process then continues to step 606, where balance transfers for aprevious period of time are identified from the available tradeline datafor the consumer. Although tradeline data may reflect a higher balanceon a credit account over time, such higher balance may simply be theresult of a transfer of a balance into the account, and are thus notindicative of a true increase in the consumer's spending. It isdifficult to confirm balance transfers based on tradeline data since theinformation available is not provided on a transaction level basis. Inaddition, there are typically lags or absences of reporting of suchvalues on tradeline reports.

Nonetheless, marketplace analysis using confirmed consumer panel andinternal consumer financial records has revealed reliable ways in whichbalance transfers into an account may be identified from imperfectindividual tradeline data alone. Three exemplary reliable methods foridentifying balance transfers from credit accounts, each which is basedin part on actual consumer data sampled, are as follows.

It should be readily apparent that the formulas (in the form recitedabove) are not necessary for all embodiments of the present process andmay vary based on the consumer data used to derive them.

A first rule identifies a balance transfer for a given consumer's creditaccount as follows. The month having the largest balance increase in thetradeline data, and which satisfies the following conditions, may beidentified as a month in which a balance transfer has occurred:

-   -   The maximum balance increase is greater than twenty times the        second maximum balance increase for the remaining months of        available data;    -   The estimated pay-down percentage calculated at step 606 above        is less than 40%; and    -   The largest balance increase is greater than $1000 based on the        available data.

A second rule identifies a balance transfer for a given consumer'scredit account in any month where the balance is above twelve times theprevious month's balance and the next month's balance differs by no morethan 20%.

A third rule identifies a balance transfer for a given consumer's creditaccount in any month where:

-   -   the current balance is greater than 1.5 times the previous        month's balance;    -   the current balance minus the previous month's balance is        greater than $4500; and    -   the estimated pay-down percent from step 606 above is less than        30%.

The process then continues to step 608, where consumer spending on eachcredit account is estimated over the next, for example, three monthperiod. In estimating consumer spend, any spending for a month in whicha balance transfer has been identified from individual tradeline dataabove is set to zero for purposes of estimating the size of theconsumer's spending wallet, reflecting the supposition that no realspending has occurred on that account. The estimated spend for each ofthe three previous months may then be calculated as follows:Estimated spend=(the current balance−the previous month's balance+(theprevious month's balance*the estimated pay-down % from step 604 above).

The exact form of the formula selected may be based on the category inwhich the consumer is identified from the model applied, and the formulais then computed iteratively for each of the three months of the firstperiod of consumer spend.

Next, at step 610, the estimated spend is then extended over, forexample, the previous three quarterly or three-month periods, providinga most-recent year of estimated spend for the consumer.

Finally, at step 612, the data output from step 610, in turn may be usedto generate a plurality of final outputs for each consumer account.These outputs may be provided in an output file that may include aportion or all of the following exemplary information, based on thecalculations above and information available from individual tradelinedata:

(i) size of previous twelve month spending wallet; (ii) size of spendingwallet for each of the last four quarters; (iii) total number ofrevolving cards, revolving balance, and average pay down percentage foreach; (iv) total number of transacting cards, and transacting balancesfor each; (v) the number of balance transfers and total estimated amountthereof; (vi) maximum revolving balance amounts and associated creditlimits; and (vii) maximum transacting balance and associated creditlimit.

After step 612, the process may end with respect to the examinedconsumer. It should be readily appreciated that the process may berepeated for any number of current consumers or consumer prospects.

Such estimated spending may be calculated in a rolling manner acrosseach previous three month (quarterly) period. For example, spending ineach of a first three months of a first quarter may be calculated basedon balance values, the category of the consumer based on the abovereferenced consumer categorization spending models and the formulas usedin steps 604 and 606. Calculation may continue every three months, usingthe previous three months' data as an input.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult, and therefore, different formula corresponding to the newcategory may be applied to the consumer for different periods of time.The rolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period. Based onthe final output generated for the consumer, commensurate purchasingincentives may be identified and provided to the consumer, for example,in anticipation of an increase in the consumer's purchasing ability asprojected by the output file. In such cases, consumers of good standing,who are categorized as transactors with a projected increase inpurchasing ability, may be offered a lower financing rate on purchasesmade during the period of expected increase in their purchasing ability,or may be offered a discount or rebate for transactions with selectedmerchants during that time.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult. Therefore, different formula corresponding to the new categorymay be applied to the consumer for different periods of time. Therolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period. Based onthe final output generated for the consumer, commensurate purchasingincentives may be identified and provided to the consumer, for example,in anticipation of an increase in the consumer's purchasing ability asprojected by the output file. In such cases, consumers of good standing,who are categorized as transactors with a projected increase inpurchasing ability, may be offered a lower financing rate on purchasesmade during the period of expected increase in their purchasing ability,or may be offered a discount or rebate for transactions with selectedmerchants during that time.

In another example, and in the case where a consumer is a revolver, aconsumer with a projected increase in purchasing ability may be offereda lower annual percentage rate on balances maintained on their creditaccount. Other like promotions and enhancements to consumers'experiences are well known and may be used within the processesdisclosed herein.

Prospective consumer populations used for modeling and/or laterevaluation may be provided from any of a plurality of availablemarketing groups, or may be culled from credit bureau data, targetedadvertising campaigns or the like. Testing and analysis may becontinuously performed to identify the optimal placement and requiredfrequency of such sources for using the size of spending walletcalculations. The processes described herein may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture.

Institutions adopting the processes disclosed herein may expect to morereadily and profitably identify opportunities for prospect and consumerofferings, which in turn provides enhanced experiences across all partsof a consumer's lifecycle. In the case of a credit provider, accurateidentification of spend opportunities allows for rapid provisioning ofcard member offerings to increase spend that, in turn, results inincreased transaction fees, interest charges and the like. The carefulselection of consumers to receive such offerings reduces the incidenceof fraud that may occur in less disciplined cardmember incentiveprograms. The reduced incidence of fraud, in turn, reduces overalloperating expenses for institutions.

As mentioned above, the process described may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture. The capacity a consumer has for spending in a variety ofcategories is the share of wallet.

The model used to determine share of wallet for particular spendcategories using the processes described herein is the share of wallet(“SoW”) model. The SoW model provides estimated data and/orcharacteristics information that is more indicative of consumer spendingpower than typical credit bureau data or scores. The SoW model mayoutput, with sufficient accuracy, data that is directly related to thespend capacity of an individual consumer. One of skill in the art willrecognize that any one or combination of the following data types, aswell as other data types, may be output by the SoW model withoutaltering the spirit and scope of the present disclosure.

The size of a consumer's twelve-month spending wallet is an exampleoutput of the SoW model. A consumer's twelve-month spending wallet maybe output as an actual or rounded dollar amount. The size of aconsumer's spending wallet for each of several consecutive quarters, forexample, the most recent four quarters, may also be output.

The SoW model output may include the total number of revolving cardsheld by a consumer, the consumer's revolving balance, and/or theconsumer's average pay-down percentage of the revolving cards. Themaximum revolving balance and associated credit limits can be determinedfor the consumer, as well as the size of the consumer's revolvingspending.

Similarly, the SoW model output may include the total number of aconsumer's transaction cards and/or the consumer's transaction balance.The SoW model may additionally output the maximum transacting balance,the associated credit limit, and/or the size of transactional spendingof the consumer.

These outputs, as well as any other outputs from the SoW model, may beappended to data profiles of a company's consumers and prospects. Theoutput enhances the company's ability to make decisions involvingprospecting, new applicant evaluation, and consumer relationshipmanagement across the consumer lifecycle. The SoW score can focus, forexample, on total spend, transaction account spend and/or a consumer'sspending trend.

Using the processes described above, balance transfers are factored outof a consumer's spend capacity. Further, when correlated with a riskscore, the SoW score may provide more insight into behaviorcharacteristics of relatively low-risk consumers and relativelyhigh-risk consumers.

The SoW score may be structured in one of several ways. For instance,the score may be a numeric score that reflects a consumer's spend invarious ranges over a given time period, such as the last quarter oryear. As an example, a score of 5000 may indicate that a consumer spentbetween $5000 and $6000 in the given time period.

The score may include a range of numbers or a numeric indicator, such asan exponent, that indicates the trend of a consumer's spend over a giventime period. For example, a trend score of +4 may indicate that aconsumer's spend has increased over the previous 4 months, while a trendscore of −4 may indicate that a consumer's spend has decreased over theprevious 4 months.

In addition to determining an overall SoW score, the SoW model outputsmay each be given individual scores and used as attributes forconsideration in credit score development by, for example, traditionalcredit bureaus. As discussed above, credit scores are traditionallybased on information in a consumer's credit bureau file.

Outputs of the SoW model, such as balance transfer information, spendcapacity and trend, and revolving balance information, could be moreindicative of risk than some traditional data elements. Therefore, acompany may use scored SoW outputs in addition to or in place oftraditional data elements when computing a final credit score. SoWoutput information may be collected, analyzed, and/or summarized in ascorecard. Such a scorecard would be useful to, for example, creditbureaus, major credit grantors, and scoring companies, such as FairIsaac Corporation of Minneapolis, Minn.

The SoW model outputs for individual consumers or small businesses canalso be used to develop various consumer models to assist in directmarketing campaigns especially targeted direct marketing campaigns. Forexample, “best consumer” or “preferred consumer” models may be developedthat correlate characteristics from the SoW model outputs, such asplastic spend, with certain consumer groups. If positive correlationsare identified, marketing and consumer relationship managementstrategies may be developed to achieve more effective results.

Outputs of the (“consumer based at lender level”) CBLL model can be usedin any business or market segment that extends credit or otherwiseevaluates the creditworthiness of a particular consumer. In oneembodiment, these businesses will be referred to herein as falling intoone of three categories: financial services companies, retail companies,and other companies.

The business cycle in each category may be divided into three phases:acquisition, retention, and disposal. The acquisition phase occurs whena business is attempting to gain new consumers. The acquisition phaseincludes, for example, targeted marketing, determining what products orservices to offer a consumer, deciding whether to lend to a particularconsumer and what the line size or loan should be, and deciding whetherto buy a particular loan. The retention phase occurs after a consumer isalready associated with the business. In the retention phase, thebusiness interests shift to managing the consumer relationship through,for example, consideration of risk, determination of credit lines,cross-sell opportunities, increasing business from that consumer, andincreasing the company's assets under management.

The disposal phase is entered when a business wishes to dissociateitself from a consumer or otherwise end the consumer relationship. Thedisposal phase can occur, for example, through settlement offers,collections, and sale of defaulted or near-default loans.

Financial services companies include, for example: banks and otherlenders, mutual fund companies, financiers of leases and sales, lifeinsurance companies, online brokerages, credit issuers, and loan buyers.

Banks and lenders can utilize the CBLL model in all phases of thebusiness cycle. One exemplary use is in relation to home equity loansand the rating given to a particular bond issue in the capital market.The CBLL model would apply to home equity lines of credit and automobileloans in a similar manner.

For example, if the holder of a home equity loan borrows from thecapital market, the loan holder issues asset-backed securities (“ABS”),or bonds, which are backed by receivables. The loan holder is thus anABS issuer. The ABS issuer applies for an ABS rating, which is assignedbased on the credit quality of the underlying receivables. One of skillin the art will recognize that the ABS issuer may apply for the ABSrating through any application means without altering the spirit andscope of the present disclosure. In assigning a rating, the ratingagencies weigh a loan's probability of default by considering thelender's underwriting and portfolio management processes. Lendersgenerally secure higher ratings by credit enhancement. Examples ofcredit enhancement include over-collateralization, buying insurance(such as wrap insurance), and structuring ABS (through, for example,senior/subordinate bond structures, sequential pay vs. pari passu, etc.)to achieve higher ratings. Lenders and rating agencies take theprobability of default into consideration when determining theappropriate level of credit enhancement.

During the acquisition phase of a loan, lenders may use the CBLL modelto improve their lending decisions. Before issuing the loan, lenders canevaluate a consumer's risk of default using the consumer's associationswith various other lenders. Evaluation leads to fewer bad loans and areduced probability of default for loans in the lender's portfolio. Alower probability of default means that, for a given loan portfolio thathas been originated using the CBLL model, either a higher rating can beobtained with the same degree of over-collateralization, or the degreeof over-collateralization can be reduced for a given debt rating. Thus,using the CBLL model at the acquisition stage of the loan reduces thelender's overall borrowing cost and loan loss reserves.

During the retention phase of a loan, the CBLL model can be used totrack a consumer's varying degree of risk. Based on the CBLL outputs,the lender can make various decisions regarding the consumerrelationship. For example, a lender may use the CBLL model to identifyborrowers who become more likely to default via the borrowers'association with other lenders. The credit lines of those borrowerswhich have not fully been drawn down can then be reduced. Selectivelyrevoking unused lines of credit may reduce the probability of defaultfor loans in a given portfolio and reduce the lender's borrowing costs.Selectively revoking unused lines of credit may also reduce the lender'srisk by minimizing further exposure to a borrower that may already be infinancial distress.

During the disposal phase of a loan, the CBLL model enables lenders tobetter predict the likelihood that a borrower will default. Once thelender has identified consumers who are in danger of default, the lendermay select those likely to repay and extend settlement offers.Additionally, lenders can use the CBLL model to identify which consumersare unlikely to pay and those who are otherwise not worth extending asettlement offer.

The CBLL model allows lenders to identify loans with risk of default,allowing lenders, prior to default, to begin anticipating a course ofaction to take if default occurs. Because freshly defaulted loans fetcha higher sale price than loans that have been non-performing for longertime periods, lenders may sell these loans earlier in the defaultperiod, thereby reducing the lender's costs.

Financiers of leases or sales, such as automobile lease or salefinanciers, can benefit from CBLL outputs in much the same way as a bankor lender, as discussed above. In typical product financing, however,the amount of the loan or lease is based on the value of the productbeing financed. Therefore, there is generally no credit limit that needsto be revisited during the course of the loan. As there is no creditlimit to be revisited, the CBLL model is most useful to lease/salesfinance companies during the acquisition and disposal phases of thebusiness cycle.

Just as the CBLL model can help loan holders determine that a particularloan is nearing default, loan buyers can use the model to evaluate thequality of a prospective purchase during the acquisition phase of thebusiness cycle. Evaluation assists the loan buyers in avoiding orreducing the sale prices of loans that are in likelihood of defaultbased on the consumer's association with other lenders.

Aspects of the retail industry for which the CBLL model would beadvantageous include, for example: retail stores having private labelcards, on-line retailers, and mail order companies.

There are two general types of credit and charge cards in themarketplace today: multipurpose cards and private label cards. A thirdtype of hybrid card is emerging. Multipurpose cards are cards that canbe used at multiple different merchants and service providers. Forexample, American Express, Visa, Mastercard, and Discover are consideredmultipurpose card issuers. Multipurpose cards are accepted by merchantsand other service providers in what is often referred to as an “opennetwork.” Transactions are routed from a point-of-sale (“POS”) through anetwork for authorization, transaction posting, and settlement.

A variety of intermediaries play different roles in the process. Theseinclude merchant processors, the brand networks, and issuer processors.An open network is often referred to as an interchange network.Multipurpose cards include a range of different card types, such ascharge cards, revolving cards, and debit cards, which are linked to aconsumer's demand deposit account (“DDA”) or checking account.

Private label cards are cards that can be used for the purchase of goodsand services from a single merchant or service provider. Historically,major department stores were the originators of private label cards.Private label cards are now offered by a wide range of retailers andother service providers. These cards are generally processed on a closednetwork, with transactions flowing between the merchant's POS and itsown backoffice or the processing center for a third-party processor.These transactions do not flow through an interchange network and arenot subject to interchange fees.

Recently, a type of hybrid card has evolved. A hybrid card, when used ata particular merchant, is that merchant's private label card, but whenused elsewhere, becomes a multipurpose card. The particular merchant'stransactions are processed in the proprietary private label network.Transactions made with the card at all other merchants and serviceproviders are processed through an interchange network.

Private label card issuers, in addition to multipurpose card issuers andhybrid card issuers, can apply the CBLL model in a similar way asdescribed above with respect to credit card companies. Knowledge of aconsumer's association with other lenders, coupled with CBLL outputs,could be used by card issuers to improve performance and profitabilityacross the entire business cycle.

Online retail and mail order companies can use the CBLL model in boththe acquisition and retention phases of the business cycle. During theacquisition phase, for example, the companies can base targetedmarketing strategies on CBLL outputs.

Targeted marketing could substantially reduce costs, especially in themail order industry, where catalogs are typically sent to a wide varietyof individuals. During the retention phase, companies can, for example,base cross-sell strategies or credit line extensions on CBLL outputs.

Types of companies which also may make use of the CBLL model include,for example and without limitation: the gaming industry, communicationsproviders, and the travel industry.

The gaming industry can use the CBLL model in, for example, theacquisition and retention phases of the business cycle. Casinos oftenextend credit to their wealthiest and/or most active players, also knownas “high rollers.” The casinos can use the CBLL model in the acquisitionphase to determine whether credit should be extended to an individual.Once credit has been extended, the casinos can use the CBLL model toperiodically review the consumer's risk of default.

Communications providers, such as telephone service providers, oftencontract into service plans with their consumers. In addition toimproving their targeted marketing strategies, communications providerscan use the CBLL outputs during the acquisition phase to determine therisk of default on a service contract associated with a potentialconsumer.

Members of the travel industry can make use of the CBLL outputs in theacquisition and retention stages of the business cycle. For example, ahotelier typically has a brand of hotel that is associated with aparticular “star-level” or class of hotel. In order to capture variousmarket segments, hoteliers may be associated with several hotel brandsthat are of different classes. During the acquisition phase of thebusiness cycle, a hotelier may use the CBLL outputs to targetindividuals that have appropriate spend capacities for various classesof hotels. During the retention phase, the hotelier may use the CBLLoutputs to determine, for example, when a particular individual's riskof default decreases. Based on that determination, the hotelier canmarket a higher class of hotel to the consumer in an attempt to convincethe consumer to upgrade.

One of skill in the relevant art(s) will recognize that many of theabove described CBLL applications may be utilized by other industriesand market segments without departing from the spirit and scope of thepresent disclosure. For example, the strategy of using CBLL to model anindustry's “best consumer” and targeting individuals sharingcharacteristics of that best consumer can be applied to nearly allindustries. CBLL data can also be used across nearly all industries toimprove consumer loyalty by reducing the number of payment reminderssent to responsible accounts.

Responsible accounts include those who are most likely to pay evenwithout being contacted by a collector. The reduction in reminders mayincrease consumer loyalty, because the consumer will not feel that thelender or service provider is unduly aggressive. The lender's or serviceprovider's collection costs are also reduced, and resources are freed todedicate to accounts requiring more persuasion.

Additionally, the CBLL model may be used in any company having a largeconsumer service call center to identify specific types of consumers.Transcripts are typically made for any call from a consumer to a callcenter. These transcripts may be scanned for specific keywords ortopics, and combined with the CBLL model to determine the consumer'scharacteristics. For example, a bank having a large consumer servicecenter may scan service calls for discussions involving bankruptcy. Thebank could then use the CBLL model with the indications from the callcenter transcripts to evaluate the consumer.

The present disclosure also includes systems for lender credit scoring,lender profiling, lender behavior analysis and modeling. These systemscan be implemented in any suitable manner using any computer, and/orover any network or communication device set forth herein. Variouscomputer implementations are described below.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the system. As those skilledin the art will appreciate, user computer may include an operatingsystem (e.g., Windows NT, 95/98/2000, XP, Vista, OS2, UNIX, Linux,Solaris, MacOS, etc.) as well as various conventional support softwareand drivers typically associated with computers. The computer mayinclude any suitable personal computer, network computer, workstation,minicomputer, mainframe or the like. User computer can be in a home orbusiness environment with access to a network. In an exemplaryembodiment, access is through a network or the Internet through acommercially-available web-browser software package.

As used herein, the term “network” includes any electroniccommunications system or method which incorporates hardware and/orsoftware components. Communication among the parties may be accomplishedthrough any suitable communication channels, such as, for example, atelephone network, an extranet, an intranet, Internet, point ofinteraction device (point of sale device, personal digital assistant(e.g., Palm Pilot®, Blackberry®), cellular phone, kiosk, etc.), onlinecommunications, satellite communications, off-line communications,wireless communications, transponder communications, local area network(LAN), wide area network (WAN), virtual private network (VPN), networkedor linked devices, keyboard, mouse and/or any suitable communication ordata input modality. Moreover, although the system is frequentlydescribed herein as being implemented with TCP/IP communicationsprotocols, the system may also be implemented using IPX, Appletalk,IP-6, NetBIOS, OSI, any tunneling protocol (e.g., IPsec, SSH), or anynumber of existing or future protocols. If the network is in the natureof a public network, such as the Internet, it may be advantageous topresume the network to be insecure and open to eavesdroppers. Specificinformation related to the protocols, standards, and applicationsoftware utilized in connection with the Internet is generally known tothose skilled in the art and, as such, need not be detailed herein. See,for example, Dilip Naik, Internet Standards and Protocols (1998); Java 2Complete, various authors, (Sybex 1999); Deborah Ray and Eric Ray,Mastering HTML 4.0 (1997); and Loshin, TCP/IP Clearly Explained (1997)and David Gourley and Brian Totty, HTTP, The Definitive Guide (2002),the contents of which are hereby incorporated by reference.

The various system components may be independently, separately orcollectively suitably coupled to the network via data links whichincludes, for example, a connection to an Internet Service Provider(ISP) over the local loop as is typically used in connection withstandard modem communication, cable modem, Dish networks, ISDN, DigitalSubscriber Line (DSL), or various wireless communication methods, see,e.g., Gilbert Held, Understanding Data Communications (1996), which ishereby incorporated by reference. It is noted that the network may beimplemented as other types of networks, such as an interactivetelevision (ITV) network. Moreover, the system contemplates the use,sale or distribution of any goods, services or information over anynetwork having similar functionality described herein.

As used herein, “transmit” may include sending electronic data from onesystem component to another over a network connection. Additionally, asused herein, “data” may include encompassing information such ascommands, queries, files, data for storage, and the like in digital orany other form.

The system contemplates uses in association with web services, utilitycomputing, pervasive and individualized computing, security and identitysolutions, autonomic computing, commodity computing, mobility andwireless solutions, open source, biometrics, grid computing and/or meshcomputing.

Any databases discussed herein may include relational, hierarchical,graphical, or object-oriented structure and/or any other databaseconfigurations. Common database products that may be used to implementthe databases include DB2 by IBM (Armonk, N.Y.), various databaseproducts available from Oracle Corporation (Redwood Shores, Ca.),Microsoft Access or Microsoft SQL Server by Microsoft Corporation(Redmond, Wash.), or any other suitable database product. Moreover, thedatabases may be organized in any suitable manner, for example, as datatables or lookup tables. Each record may be a single file, a series offiles, a linked series of data fields or any other data structure.Association of certain data may be accomplished through any desired dataassociation technique such as those known or practiced in the art. Forexample, the association may be accomplished either manually orautomatically. Automatic association techniques may include, forexample, a database search, a database merge, GREP, AGREP, SQL, using akey field in the tables to speed searches, sequential searches throughall the tables and files, sorting records in the file according to aknown order to simplify lookup, and/or the like. The association stepmay be accomplished by a database merge function, for example, using a“key field” in pre-selected databases or data sectors.

More particularly, a “key field” partitions the database according tothe high-level class of objects defined by the key field. For example,certain types of data may be designated as a key field in a plurality ofrelated data tables and the data tables may then be linked on the basisof the type of data in the key field. The data corresponding to the keyfield in each of the linked data tables is preferably the same or of thesame type. However, data tables having similar, though not identical,data in the key fields may also be linked by using AGREP, for example.In accordance with one embodiment, any suitable data storage techniquemay be utilized to store data without a standard format. Data sets maybe stored using any suitable technique, including, for example, storingindividual files using an ISO/IEC 7816-4 file structure; implementing adomain whereby a dedicated file is selected that exposes one or moreelementary files containing one or more data sets; using data setsstored in individual files using a hierarchical filing system; data setsstored as records in a single file (including compression, SQLaccessible, hashed via one or more keys, numeric, alphabetical by firsttuple, etc.); Binary Large Object (BLOB); stored as ungrouped dataelements encoded using ISO/IEC 7816-6 data elements; stored as ungroupeddata elements encoded using ISO/IEC Abstract Syntax Notation (ASN.1) asin ISO/IEC 8824 and 8825; and/or other proprietary techniques that mayinclude fractal compression methods, image compression methods, etc.

In one exemplary embodiment, the ability to store a wide variety ofinformation in different formats is facilitated by storing theinformation as a BLOB. Thus, any binary information can be stored in astorage space associated with a data set. As discussed above, the binaryinformation may be stored on the financial transaction instrument orexternal to but affiliated with the financial transaction instrument.The BLOB method may store data sets as ungrouped data elements formattedas a block of binary via a fixed memory offset using either fixedstorage allocation, circular queue techniques, or best practices withrespect to memory management (e.g., paged memory, least recently used,etc.). By using BLOB methods, the ability to store various data setsthat have different formats facilitates the storage of data associatedwith the financial transaction instrument by multiple and unrelatedowners of the data sets. For example, a first data set which may bestored may be provided by a first party, a second data set which may bestored may be provided by an unrelated second party, and yet a thirddata set which may be stored, may be provided by an third partyunrelated to the first and second party. Each of these three exemplarydata sets may contain different information that is stored usingdifferent data storage formats and/or techniques. Further, each data setmay contain subsets of data that also may be distinct from othersubsets.

As stated above, in various embodiments, the data can be stored withoutregard to a common format. However, in one exemplary embodiment, thedata set (e.g., BLOB) may be annotated in a standard manner whenprovided for manipulating the data onto the financial transactioninstrument. The annotation may comprise a short header, trailer, orother appropriate indicator related to each data set that is configuredto convey information useful in managing the various data sets. Forexample, the annotation may be called a “condition header”, “header”,“trailer”, or “status”, herein, and may comprise an indication of thestatus of the data set or may include an identifier correlated to aspecific issuer or owner of the data. In one example, the first threebytes of each data set BLOB may be configured or configurable toindicate the status of that particular data set; e.g., LOADED,INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequent bytes ofdata may be used to indicate for example, the identity of the issuer,user, transaction/membership account identifier or the like. Each ofthese condition annotations are further discussed herein.

The data set annotation may also be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

The data, including the header or trailer may be received by a standalone interaction device configured to add, delete, modify, or augmentthe data in accordance with the header or trailer. As such, in oneembodiment, the header or trailer is not stored on the transactiondevice along with the associated issuer-owned data but instead theappropriate action may be taken by providing to the transactioninstrument user at the stand alone device, the appropriate option forthe action to be taken. The system may contemplate a data storagearrangement wherein the header or trailer, or header or trailer history,of the data is stored on the transaction instrument in relation to theappropriate data.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of thesystem may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.

The computing unit of the web client may be further equipped with anInternet browser connected to the Internet or an intranet using standarddial-up, cable, DSL or any other Internet protocol known in the art.Transactions originating at a web client may pass through a firewall inorder to prevent unauthorized access from users of other networks.Further, additional firewalls may be deployed between the varyingcomponents of the system to further enhance security.

Firewall may include any hardware and/or software suitably configured toprotect system components and/or enterprise computing resources fromusers of other networks. Further, a firewall may be configured to limitor restrict access to various systems and components behind the firewallfor web clients connecting through a web server. Firewall may reside invarying configurations including Stateful Inspection, Proxy based andPacket Filtering among others. Firewall may be integrated within a webserver or any other system components or may further reside as aseparate entity. A firewall may implement network address translation(“NAT”) and/or network address port translation (“NAPT”). A firewall mayaccommodate various tunneling protocols to facilitate securecommunications, such as those used in virtual private networking. Afirewall may implement a demilitarized zone (“DMZ”) to facilitatecommunications with a public network such as the Internet. A firewallmay be integrated as software within an Internet server, any otherapplication server components or may reside within another computingdevice or may take the form of a standalone hardware component.

The computers discussed herein may provide a suitable website or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a website having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that might be used to interact with theuser. For example, a typical website might include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), common gateway interface scripts (CGI), extensiblemarkup language (XML), dynamic HTML, cascading style sheets (CSS),helper applications, plug-ins, and the like. A server may include a webservice that receives a request from a web server, the request includinga URL (http://yahoo.com/stockquotes/ge) and an IP address(123.56.789.234). The web server retrieves the appropriate web pages andsends the data or applications for the web pages to the IP address. Webservices are applications that are capable of interacting with otherapplications over a communications means, such as the internet. Webservices are typically based on standards or protocols such as XML,SOAP, WSDL and UDDI. Web services methods are well known in the art, andare covered in many standard texts. See, e.g., Alex Nghiem, IT WebServices: A Roadmap for the Enterprise (2003), hereby incorporated byreference.

Practitioners will also appreciate that there are a number of methodsfor displaying data within a browser-based document. Data may berepresented as standard text or within a fixed list, scrollable list,drop-down list, editable text field, fixed text field, pop-up window,and the like. Likewise, there are a number of methods available formodifying data in a web page such as, for example, free text entry usinga keyboard, selection of menu items, check boxes, option boxes, and thelike.

The system and method may be described herein in terms of functionalblock components, screen shots, optional selections and variousprocessing steps. It should be appreciated that such functional blocksmay be realized by any number of hardware and/or software componentsconfigured to perform the specified functions. For example, the systemmay employ various integrated circuit components, e.g., memory elements,processing elements, logic elements, look-up tables, and the like, whichmay carry out a variety of functions under the control of one or moremicroprocessors or other control devices. Similarly, the softwareelements of the system may be implemented with any programming orscripting language such as C, C++, Macromedia Cold Fusion, MicrosoftActive Server Pages, Java, COBOL, assembler, PERL, Visual Basic, SQLStored Procedures, extensible markup language (XML), with the variousalgorithms being implemented with any combination of data structures,objects, processes, routines or other programming elements. Further, itshould be noted that the system may employ any number of conventionaltechniques for data transmission, signaling, data processing, networkcontrol, and the like. Still further, the system could be used to detector prevent security issues with a client-side scripting language, suchas JavaScript, VBScript or the like. For a basic introduction ofcryptography and network security, see any of the following references:(1) “Applied Cryptography: Protocols, Algorithms, And Source Code In C,”by Bruce Schneier, published by John Wiley & Sons (second edition,1995); (2) “Java Cryptography” by Jonathan Knudson, published byO'Reilly & Associates (1998); (3) “Cryptography & Network Security:Principles & Practice” by William Stallings, published by Prentice Hall;all of which are hereby incorporated by reference.

As will be appreciated by one of ordinary skill in the art, the systemmay be embodied as a customization of an existing system, an add-onproduct, upgraded software, a stand alone system, a distributed system,a method, a data processing system, a device for data processing, and/ora computer program product. Accordingly, the system may take the form ofan entirely software embodiment, an entirely hardware embodiment, or anembodiment combining aspects of both software and hardware. Furthermore,the system may take the form of a computer program product on acomputer-readable storage medium having computer-readable program codemeans embodied in the storage medium. Any suitable computer-readablestorage medium may be utilized, including hard disks, CD-ROM, opticalstorage devices, magnetic storage devices, and/or the like.

The system and method is described herein with reference to screenshots, block diagrams and flowchart illustrations of methods, apparatus(e.g., systems), and computer program products according to variousembodiments. It will be understood that each functional block of theblock diagrams and the flowchart illustrations, and combinations offunctional blocks in the block diagrams and flowchart illustrations,respectively, can be implemented by computer program instructions.

The systems and methods described herein with reference to process flowsand screenshots depicted are merely embodiments and are not intended tolimit the scope of the disclosure as described herein. For example, thesteps recited in any of the method or process descriptions may beexecuted in any order and are not limited to the order presented. Itwill be appreciated that the following description makes appropriatereferences not only to the steps and user interface, but also to thevarious system components as described above.

These computer program instructions may be loaded onto a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructionsthat execute on the computer or other programmable data processingapparatus create means for implementing the functions specified in theflowchart block or blocks. These computer program instructions may alsobe stored in a computer-readable memory that can direct a computer orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, webpages, websites, web forms, prompts, etc. Practitionerswill appreciate that the illustrated steps described herein may comprisein any number of configurations including the use of windows, webpages,web forms, popup windows, prompts and the like. It should be furtherappreciated that the multiple steps as illustrated and described may becombined into single webpages and/or windows but have been expanded forthe sake of simplicity. In other cases, steps illustrated and describedas single process steps may be separated into multiple webpages and/orwindows but have been combined for simplicity.

Furthermore, individual system components may take the form of acomputer program product on a computer-readable storage medium havingcomputer-readable program code means embodied in the storage medium. Anysuitable computer-readable storage medium may be utilized, includinghard disks, CD-ROM, optical storage devices, magnetic storage devices,and/or the like.

While the steps outlined above represent a specific embodiment,practitioners will appreciate that there are any number of computingalgorithms and user interfaces that may be applied to create similarresults. The steps are presented for the sake of explanation only andare not intended to limit the scope of the disclosure in any way.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of any or all the claims or the disclosure. Itshould be understood that the detailed description and specificexamples, indicating exemplary embodiments, are given for purposes ofillustration only and not as limitations. Many changes and modificationswithin the scope of the instant disclosure may be made without departingfrom the spirit thereof, and the disclosure includes all suchmodifications. Corresponding structures, materials, acts, andequivalents of all elements in the claims below are intended to includeany structure, material, or acts for performing the functions incombination with other claim elements as specifically claimed. The scopeof the disclosure should be determined by the appended claims and theirlegal equivalents, rather than by the examples given above. Reference toan element in the singular is not intended to mean “one and only one”unless explicitly so stated, but rather “one or more.” Moreover, where aphrase similar to ‘at least one of A, B, and C’ is used in the claims,it is intended that the phrase be interpreted to mean that A alone maybe present in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the disclosure hasbeen described as a method, it is contemplated that it may be embodiedas computer program instructions on a tangible computer-readablecarrier, such as a magnetic or optical memory or a magnetic or opticaldisk. All structural, chemical, and functional equivalents to theelements of the above-described exemplary embodiments that are known tothose of ordinary skill in the art are expressly incorporated herein byreference and are intended to be encompassed by the present claims.Moreover, it is not necessary for a device or method to address each andevery problem sought to be solved by the present disclosure, for it tobe encompassed by the present claims. Furthermore, no element,component, or method step in the present disclosure is intended to bededicated to the public regardless of whether the element, component, ormethod step is explicitly recited in the claims. No claim element hereinis to be construed under the provisions of 35 U.S.C. 112, sixthparagraph, unless the element is expressly recited using the phrase“means for.” As used herein, the terms “comprises”, “comprising”, or anyother variation thereof, are intended to cover a non-exclusiveinclusion, such that a process, method, article, or apparatus thatcomprises a list of elements does not include only those elements butmay include other elements not expressly listed or inherent to suchprocess, method, article, or apparatus.

We claim:
 1. An account default prediction method comprising:calculating, by a computer based system for account default prediction,a first lender default risk factor based upon a first consumer defaultrisk factor associated with a first consumer, first automobile loan dataassociated with said first consumer, a second consumer default riskfactor associated with a second consumer, and second automobile loandata associated with said second consumer, wherein said computer basedsystem comprises a processor and a tangible, non-transitory memory;calculating, by said computer based system, a second lender default riskfactor based upon a third consumer default risk factor associated with athird consumer, third automobile loan data associated with said thirdconsumer, a fourth consumer default risk factor associated with a fourthconsumer, and fourth automobile loan data associated with said fourthconsumer, ranking, by said computer based system, said first lender andsaid second lender based on said first lender default risk factor andsaid second lender default risk factor to create a default risk factorranking; determining, by said computer based system, firstinsufficiencies associated with said first lender and said secondinsufficiencies associated with said second lender; creating, by saidcomputer based system, an insufficiency ranking based upon said firstlender insufficiencies and said second lender insufficiencies;determining, by said computer based system, that a fifth consumer isassociated with at least one said first lender and said second lender;creating, by said computer based system, a final risk index based uponsaid default risk factor ranking and said insufficiency ranking; anddetermining, by said computer based system, said account defaultprediction based upon said association of said fifth consumer with atleast one of said first lender and said second lender and said finalrisk index.
 2. The method of claim 1, further comprising, determining astrategy to interact with said fifth consumer based upon saidassociation of said fifth consumer with at least one of said firstlender and said second lender.
 3. The method of claim 2, wherein saidstrategy further comprises making automobile loan approval decisions forsaid fifth consumer based upon said association of said fifth consumerwith at least one of said first lender and said second lender.
 4. Themethod of claim 2, wherein said strategy further comprises discontinuinga relationship with said fifth consumer upon said association of saidfifth consumer with at least one of said first lender and said secondlender.
 5. The method of claim 2, wherein said strategy furthercomprises soliciting said fifth consumer for an additional automobileloan in accordance with association of said fifth consumer with at leastone of said first lender and said second lender.
 6. The method of claim1, wherein said obtaining a first consumer default risk factor furthercomprises calculating a comprehensive consumer default risk value. 7.The method of claim 6, wherein said calculating a comprehensive consumerdefault risk value further comprises: obtaining consumer credit datarelating to said first consumer; modeling consumer spending patterns ofsaid first consumer using said consumer credit data to obtain anestimated spend capacity of said first consumer; and calculating acomprehensive consumer default risk value for said first consumer basedupon said consumer credit data and said estimated spend capacity.
 8. Themethod of claim 7, wherein said calculating a comprehensive consumerdefault risk value further comprises: obtaining internal data relatingto said first consumer; and further calculating said comprehensiveconsumer default risk value for said first consumer based upon saidconsumer credit data, said internal data and said estimated spendcapacity.
 9. An article of manufacture including a computer readablemedium having instructions stored thereon that, in response to executionby a computing device for account default prediction, cause thecomputing device to perform operations comprising: calculating, by saidcomputing device comprising a processor and a tangible, non-transitorymemory, a first lender default risk factor based upon a first consumerdefault risk factor associated with a first consumer, first automobileloan data associated with said first consumer, a second consumer defaultrisk factor associated with a second consumer, and second automobileloan data associated with said second consumer, calculating, by saidcomputing device, a second lender default risk factor based upon a thirdconsumer default risk factor associated with a third consumer, thirdautomobile loan data associated with said third consumer, a fourthconsumer default risk factor associated with a fourth consumer, andfourth automobile loan data associated with said fourth consumer,ranking, by said computing device, said first lender and said secondlender based on said first lender default risk factor and said secondlender default risk factor to create a default risk factor ranking;determining, by said computing device, first insufficiencies associatedwith said first lender and said second insufficiencies associated withsaid second lender; creating, by said computing device, an insufficiencyranking based upon said first lender insufficiencies and said secondlender insufficiencies; determining, by said computing device, that afifth consumer is associated with at least one said first lender andsaid second lender; creating, by said computing device, a final riskindex based upon said default risk factor ranking and said insufficiencyranking; and determining, by said computing device, said account defaultprediction based upon said association of said fifth consumer with atleast one of said first lender and said second lender and said finalrisk index.
 10. The article of manufacture of claim 9, furthercomprising, determining a strategy to interact with said fifth consumerbased upon said association of said fifth consumer with at least one ofsaid first lender and said second lender.
 11. The article of manufactureof claim 10, wherein said strategy further comprises making automobileloan approval decisions for an automobile loan associated with saidfifth consumer based upon said association of said fifth consumer withat least one of said first lender and said second lender.
 12. Thearticle of manufacture of claim 10, wherein said strategy furthercomprises discontinuing a relationship with said fifth consumer uponsaid association of said fifth consumer with at least one of said firstlender and said second lender.
 13. The article of manufacture of claim10, wherein said strategy further comprises soliciting said fifthconsumer for an additional automobile loan in accordance withassociation of said fifth consumer with at least one of said firstlender and said second lender.
 14. The article of manufacture of claim9, wherein said obtaining a first consumer default risk factor furthercomprises calculating a comprehensive consumer default risk value. 15.The article of manufacture of claim 14, wherein said calculating acomprehensive consumer default risk value further comprises: obtainingconsumer credit data relating to said first consumer; modeling consumerspending patterns of said first consumer using said consumer credit datato obtain an estimated spend capacity of said first consumer; andcalculating a comprehensive consumer default risk value for said firstconsumer based upon said consumer credit data and said estimated spendcapacity.
 16. The article of manufacture of claim 15, wherein saidcalculating a comprehensive consumer default risk value furthercomprises: obtaining internal data relating to said first consumer; andfurther calculating said comprehensive consumer default risk value forsaid first consumer based upon said consumer credit data, said internaldata and said estimated spend capacity.
 17. A system comprising: atangible, non-transitory memory communicating with a processor foraccount default prediction, said tangible, non-transitory memory havinginstructions stored thereon that, in response to execution by saidprocessor, cause said processor to perform operations comprising:calculating, by said processor, a first lender default risk factor basedupon a first consumer default risk factor associated with a firstconsumer, first automobile loan data associated with said firstconsumer, a second consumer default risk factor associated with a secondconsumer, and second automobile loan data associated with said secondconsumer, calculating, by said processor, a second lender default riskfactor based upon a third consumer default risk factor associated with athird consumer, third automobile loan data associated with said thirdconsumer, a fourth consumer default risk factor associated with a fourthconsumer, and fourth automobile loan data associated with said fourthconsumer, ranking, by said processor, said first lender and said secondlender based on said first lender default risk factor and said secondlender default risk factor to create a default risk factor ranking;determining, by said processor, first insufficiencies associated withsaid first lender and said second insufficiencies associated with saidsecond lender; creating, by said processor, an insufficiency rankingbased upon said first lender insufficiencies and said second lenderinsufficiencies; determining, by said processor, that a fifth consumeris associated with at least one said first lender and said secondlender; creating, by said processor, a final risk index based upon saiddefault risk factor ranking and said insufficiency ranking; anddetermining, by said processor, said account default prediction basedupon said association of said fifth consumer with at least one of saidfirst lender and said second lender and said final risk index.
 18. Thesystem of claim 17, further comprising, determining a strategy tointeract with said fifth consumer based upon said association of saidfifth consumer with at least one of said first lender and said secondlender.
 19. The system of claim 18, wherein said strategy furthercomprises making automobile loan approval decisions for an automobileloan associated with said fifth consumer based upon said association ofsaid fifth consumer with at least one of said first lender and saidsecond lender.
 20. The system of claim 18, wherein said strategy furthercomprises soliciting said fifth consumer for an additional automobileloan in accordance with association of said fifth consumer with at leastone of said first lender and said second lender.